Node stability from bootstrap community assignments

Computes per-node stability given the empirical community structure and the homogenized bootstrap memberships contained in a mixMN_fit object. Stability is expressed as the proportion of bootstrap replications that assign each node to its empirical (original) community.

Usage

membershipStab(fit)

Arguments

fit

An object returned by mixMN() (class mixMN_fit), containing $communities$original_membership and $communities$boot_memberships. Bootstrap memberships must be available, i.e. reps > 0 and "community" %in% boot_what.

Value

An object of class c("membershipStab"), with components:

membership

List with:

empirical

Named integer vector of empirical community labels

bootstrap

Matrix of homogenized bootstrap labels (reps × p)

membership.stability

List with:

empirical.dimensions

Named numeric vector of node-level stability (proportion assigned to empirical community)

all.dimensions

Matrix (p × K) with proportions of assignment to each community

community_palette

Named vector of colors for communities, if available

Details

Bootstrap community labels are first aligned to the empirical solution using EGAnet::community.homogenize(). Stability is then computed node-wise as the proportion of bootstrap runs in which the node's community matches its empirical assignment.

The returned object has class "membershipStab" and provides print(), summary(), and plot() methods for quick inspection, descriptive summaries, and visualization of node stability.

References

Christensen, A. P., & Golino, H. (2021). Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial. Psych, 3(3), 479–500. doi:10.3390/psych3030032